Formation of Non-Agglomerated Titania Nanoparticles in a Flame Reactor
Bibliographic record
Abstract
The formation of non-agglomerated titania particles by oxidation of titanium-tetra-isopropoxide (TTIP) has been studied in a methane/oxygen coflow diffusion-flame reactor. A change in the proportion of virtually non-agglomerated particles in TEM images was observed using a rapid cooling of the entire flame aerosol with a blow of cold Ar quenching gas and supercooling in a Laval nozzle placed above the flame. The proportion of non-agglomerates was 25% for TiO2 particles produced without any cooling steps. When the quenching gas of 25 L/min Ar cooled at −70°C blew on the tip of the flame, the proportion of non-agglomerates was 70%. When the flame aerosol was supercooled in the Laval nozzle after blowing −70°C Ar quenching gas, a decrease in the aerosol temperature was induced from approximately 1500 to 300°C in 0.9 ms and, as a result, the proportion of non-agglomerates was as large as 90%. It was found that the rapid cooling in the region of the flame tip is quite effective for preventing agglomeration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".